Mobile remote sensing platforms are a rich source of geographical data. Such mobile platforms may include aerial collection platforms such as aircraft and satellites in low-earth orbits (LEO), medium earth orbits (MEO), or geosynchronous/geostationary orbits. Mobile platforms use one or more sensors to collect geographical data. These sensors have a wide variety of sensor characteristics, including bandwidth, wavelengths resolution, and sensing technique, depending upon the application and information desired.
With regard to sensing techniques, the mobile remote sensing platform sensor(s) may be passive (e.g. simply sense energy emitted from targets). With regard to wavelengths, sensors may operate in a variety of bandwidths including ultraviolet, visual and infrared (near-infrared (NIR), short wave infrared (SWIR) and long wave infrared (LWIR). Sensor resolution may be defined in terms of spatial resolution (e.g. the pixel size of an image representing the area of the imaged surface as determined by the sensors' instantaneous field of view also referred to as ground sample distance (GSD); spectral resolution (e.g. the number of wavelengths (bands) collected), temporal resolution (e.g. the time period between measurements) and radiometric resolution (e.g. the effective bit depth or dynamic range of the sensor).
In many cases, mobile remote sensing platforms are used to search for and find “target” geographical features in a particular area. For example, a mobile remote sensing platform may be used to determine the extent of damage caused by a recent forest fire. In such cases, the sensor characteristics are usually chosen to maximize discriminants between the “target” and the background. Because the characteristics of the target and the background are typically not known apriori, this can be problematic, particularly with mobile remote sensing platforms, especially those mobile platforms that cannot be remotely configured in real or near real time.
One potential solution to this problem is to use hyperspectral imagers. Like other sensors or sensor suites, hyperspectral imagers collect and process data from across the electromagnetic wave spectrum. But unlike other multispectral imagers (which measure radiation reflected from a surface at a few wide, separated wavelength bands) hyperspectral imagers measure reflected radiation at a series of narrow and (typically) contiguous wavelength bands. This permits the gathering of more detailed spectral information which can provide much more information about the surface than a multispectral sensor and can reduce the guesswork in choosing how to best spectrally configure the mobile remote sensing platform sensor to collect information of interest.
However, this solution can severely stress hyperspectral imaging processing requirements on the mobile remote sensing platform and/or bandwidth and latency requirements of the communication link between the mobile remote sensing platform and the base station. Furthermore, while hyperspectral sensing might ease the planning of which spectral bands to collect, they do not solve and may well worsen sensor resolution and update concerns.
Therefore it would be desirable to have a system and method that takes into account at least some of the issues discussed above, as well as other possible issues.